A similarity-based learning algorithm for fuzzy system identification with a two-layer optimization scheme

Sj Lee*, Xiao Jun Zeng

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

This paper presents a similarity-based fuzzy learning approach with a two-layer optimization scheme to make fuzzy systems more compact and accuracy. Two ways to improve fuzzy learning algorithms are considered in this paper, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned, the purpose aims at refining the fuzzy rule base by the similarity analysis of fuzzy sets, fuzzy numbers, fuzzy membership functions or fuzzy rules. Through the similarity analysis, the complete rules can be probably kept by decreasing the redundant rules in the rule base of fuzzy systems. Moreover, the optimization scheme can be regarded as a two-layer parameters optimization in the entire work, because the parameters of the initial fuzzy model have been fine tuning by two phases gradation on layer for discovering a better local minimum.

原文English
主出版物標題2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
DOIs
出版狀態Published - 23 10月 2012
事件2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012 - Brisbane, QLD, Australia
持續時間: 10 6月 201215 6月 2012

出版系列

名字IEEE International Conference on Fuzzy Systems
ISSN(列印)1098-7584

Conference

Conference2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
國家/地區Australia
城市Brisbane, QLD
期間10/06/1215/06/12

指紋

深入研究「A similarity-based learning algorithm for fuzzy system identification with a two-layer optimization scheme」主題。共同形成了獨特的指紋。

引用此